Soft robotic tail uses learning to improve motion control and timing
Learning-Based Pressure Predictive Control of a Vertebraic Soft Robotic Tail
Robotics
Summary
Soft robots can bend and stretch safely but are hard to control because their movements are complex and materials don’t behave simply. The authors created a learning-based control system that predicts how much air pressure to use to make a soft robotic tail move as desired, even during quick movements. Their approach improves accuracy by nearly 70% and speeds up calculation time by about 60%, helping the tail move in sync with a robotic dog’s body. This method can help the soft tail interact better with its surroundings.
What this means in practice
- •For robotic system integrators: Coordinate soft robot appendages with rigid-bodied robots by predicting pressure inputs to improve control accuracy and timing.
- •For industrial automation engineers: Enhance flexible grippers or manipulators by using predictive pressure control to achieve smoother interactions with varying objects.
Authors
Wenjian Yang, Nan Huang, Yukang Nie, Fang Chen, Wanchao Chi, Jiansheng Dai, Sicong Liu
Abstract
Soft robots have attracted much attention for their safe human-robot interaction and flexibility, but the typical continuum structure and nonlinear material behavior make the kinematics modelling complex, especially in non-static motions. In this work, we proposed an LSTM-based pressure predictive control (PPC) for the motion control of a vertebraic soft robotic tail and the coordination with a quadruped robot. The PPC consists of an inverse kinematics (IK) model, a forward kinematics (FK) model and a pressure compensation (P-comp) model, and achieves non-static and quasi-static motion control of the tail. Compared with the IK-only model, the average RMSE of the PPC's simulation trajectories reduces by 69.8%, when executing target trajectories. In the coordinated motions of the soft tail quadruped, using a prediction data set to train the PPC enables next-moment action prediction and reduces computation time by 60.9%, which enhances the real-time response of the tail to match the quadruped torso's moving rate. The PPC provides a simple and effective method to model the soft tail for both non-static and quasi-static motion control, and grants the soft tail quadruped with the functionality of interacting with the environment.